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Kodogiannis, Vassilis; Lygouras, John N.; Tarczynski, Andrzej; Chowdrey, Hardial S.
Publisher: IEEE
Languages: English
Types: Article
Subjects: UOW3, UOW2
Current clinical diagnostics are based on biochemical, immunological or microbiological methods. However, these methods are operator dependent, time consuming, expensive and require special skills, and are therefore not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect Urinary Tract Infection from 45 suspected cases that were sent for analysis in a UK Public Health Registry. These samples were analysed by incubation in a volatile generation test tube system for 4-5h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified Expectation Maximisation scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial ontaminants in urine samples using electronic nose technology.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] V. Moret-Bonillo, “Integration of data information and knowledge in intelligent patient monitoring,” Expert Syst. Appl., vol. 15, pp. 155-163, 1998.
    • [2] M. Phillips, “Method for the collection and assay of volatile organic compounds in breath,” Anal. Biochem., vol. 247, pp. 272-278, 1997.
    • [3] S. Ampuero and J. Bosset, “The electronic nose applied to dairy products: A review,” Sens. Actuators B, vol. 94, pp. 1-12, 2003.
    • [4] W. Gopel, “Chemical imaging: I. Concepts and visions for electronic and bioelectronic noses,” Sens. Actuators B, vol. 52, pp. 125-142, 1998.
    • [5] K. Persaud, A. M. Pisanelli, P. Evans, and P. Travers, “Monitoring urinary tract infections and bacterial vaginosis,” Sens. Actuators B, vol. 116, pp. 116-120, 2006.
    • [6] A. K. Pavlou, V. S. Kodogiannis, and A. P. F. Turner, “Intelligent classification of bacterial clinical isolates in vitro, using electronic noses,” in Proc. Int. Conf. Neural Netw. Expert Syst. Med. HealthCare, 2001, pp. 231-237.
    • [7] A. K. Pavlou, N. Magan, D. Sharp, J. Brown, H. Barr, and A. P. F. Turner, “An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro,” Biosens. Bioelectron., vol. 15, pp. 333-342, 2000.
    • [8] R. Fend, C. Bessant, A. J. Williams, and A. C. Woodman, “Monitoring haemodialysis using electronic nose and chemometrics,” Biosens. Bioelectron., vol. 19, no. 12, pp. 1581-1590, 2004.
    • [9] V. S. Kodogiannis, A. K. Pavlou, P. Chountas, and A.P. F. Turner, “Evolutionary computing techniques for diagnosis of urinary tract infections in vivo, using gas sensors,” in Neural Computing and Soft Computing (Advance in Soft Computing), New York, 2003, pp. 474-479.
    • [10] M. Sugeno, “Fuzzy measures and fuzzy integrals: A survey,” in Fuzzy Automata and Decision Processes, M. M. Gupta, G. N. Saridis, and B. R. Gaines, Eds. Amsterdam, The Netherlands: North Holland, 1977, pp. 89-102.
    • [11] S. Mitra, S. K. Pal, and P. Mitra, “Data mining in soft computing framework: A survey,” IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 3-14, Jan. 2002.
    • [12] L. I. Kuncheva, Fuzzy Classifier Design. Heidelberg, Germany: Physica-Verlag, 2000.
    • [13] S. Chen, A. Billings, and P. M. Grant, “Orthogonal least squares algorithm for radial basis function networks,” IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302-309, Mar. 1991.
    • [14] E. Wadge, V. S. Kodogiannis, and D. Tomtsis, “Neuro-fuzzy ellipsoid basis function multiple classifier for diagnosis of urinary tract infections,” in Proc. ICCMSE 2003. Kastoria, Greece, pp. 673-677.
    • [15] E. Alpaydin, “Soft vector quantization and the EM algorithm,” Neural Netw., vol. 11, no. 3, pp. 467-477, 1998.
    • [16] T. K. Moon, “The expectation-maximization algorithm,” IEEE Signal Process. Mag., vol. 13, no. 6, pp. 47-60, Nov. 1996.
    • [17] N. Ueda and R. Nakano, “EM algorithm with split and merge operations for mixture models,” IEIC Trans. Inf. Syst., vol. 83, no. 12, pp. 2047-2055, 2000.
    • [18] E. Wadge, “The use of EM-Based neural network schemes for modelling and classification,” Ph.D. dissertation, Westminster Univ., London, U.K., 2005.
    • [19] R. Fend, “Development of medical point-of-care applications for renal medicine and tuberculosis based on electronic nose technology,” Ph.D. dissertation, Cranfield Univ., Cranfield, U.K. 2004.
    • [20] D. Tax, M. van Breukelen, R. Duin, and J. Kittler, “Combining multiple classifiers by averaging or by multiplying?,” Pattern Recognit., vol. 33, pp. 1475-1485, 2000.
    • [21] P. Szczepaniak, P. Lisboa, and J. Kacprzyk, Fuzzy Systems in Medicine. New York: Springer-Verlag, 2000. Vassilis S. Kodogiannis (M'01) received the Elec. Eng. degree from Democritus University of Thrace, Xanthi, Greece, in 1990, the M.Sc. degree in very large-scale integration (VLSI) systems engineering from the University of Manchester Institute of Science and Technology (UMIST), Manchester, U.K., in 1991, and the Ph.D. degree in electrical engineering from Liverpool University, Liverpool, U.K., in 1994. He is currently a Principal Lecturer in the School of Computer Science, University of Westminster, London, U.K. His current research interests include the areas of intelligent systems, image processing, and control. Dr. Kodogiannis is a member of the Technical Chamber of Greece.
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